


import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=sys.maxsize)  -> Any:
											a__      ='bilinear'
											a__      =max_size
											a__      =short_edge_length





				def __call__(				self       ,  lowercase_)  -> List[Any]:
											a__      =[]
											for img in imgs:
																		a__    ,     a__      =img.shape[:2]
																		# later: provide list and randomly choose index for resize
																		a__      =np.random.randint(self.short_edge_length[0]       ,  self.short_edge_length[1] + 1)
																		if size == 0:
																									return img
																		a__      =size * 1.0 / min(lowercase_       ,  lowercase_)
																		if h < w:
																									a__    ,     a__      =size, scale * w
																		else:
																									a__    ,     a__      =scale * h, size
																		if max(lowercase_       ,  lowercase_) > self.max_size:
																									a__      =self.max_size * 1.0 / max(lowercase_       ,  lowercase_)
																									a__      =newh * scale
																									a__      =neww * scale
																		a__      =int(neww + 0.5)
																		a__      =int(newh + 0.5)

																		if img.dtype == np.uinta:
																									a__      =Image.fromarray(lowercase_)
																									a__      =pil_image.resize((neww, newh)       ,  PILImageResampling.BILINEAR)
																									a__      =np.asarray(lowercase_)
																		else:
																									a__      =img.permute(2       ,  0       ,  1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
																									a__      =nn.functional.interpolate(
																									    lowercase_       ,  (newh, neww)       ,  mode=self.interp_method       ,  align_corners=lowercase_).squeeze(0)
																		img_augs.append(lowercase_)

											return img_augs







class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> Any:
											a__      =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,  cfg.INPUT.MAX_SIZE_TEST)
											a__      =cfg.INPUT.FORMAT
											a__      =cfg.SIZE_DIVISIBILITY
											a__      =cfg.PAD_VALUE
											a__      =cfg.INPUT.MAX_SIZE_TEST
											a__      =cfg.MODEL.DEVICE
											a__      =torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)       ,  1       ,  1)
											a__      =torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)       ,  1       ,  1)
											a__      =lambda lowercase_: (x - self.pixel_mean) / self.pixel_std





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Tuple:
											a__      =tuple(max(lowercase_) for s in zip(*[img.shape for img in images]))
											a__      =[im.shape[-2:] for im in images]
											a__      =[
											    nn.functional.pad(
											        lowercase_       ,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,  value=self.pad_value       ,  )
											    for size, im in zip(lowercase_       ,  lowercase_)
											]

											return torch.stack(lowercase_), torch.tensor(lowercase_)





				def __call__(				self       ,  lowercase_       ,  lowercase_=False)  -> Dict:
											with torch.no_grad():
																		if not isinstance(lowercase_       ,  lowercase_):
																									a__      =[images]
																		if single_image:
																									assert len(lowercase_) == 1
																		for i in range(len(lowercase_)):
																									if isinstance(images[i]       ,  torch.Tensor):
																																images.insert(lowercase_       ,  images.pop(lowercase_).to(self.device).float())
																									elif not isinstance(images[i]       ,  torch.Tensor):
																																images.insert(
																																    lowercase_       ,  torch.as_tensor(img_tensorize(images.pop(lowercase_)       ,  input_format=self.input_format))
																																    .to(self.device)
																																    .float()       ,  )
            # resize smallest edge
																		a__      =torch.tensor([im.shape[:2] for im in images])
																		a__      =self.aug(lowercase_)
																		# transpose images and convert to torch tensors
																		# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																		# now normalize before pad to avoid useless arithmetic
																		a__      =[self.normalizer(lowercase_) for x in images]
																		# now pad them to do the following operations
																		a__    ,     a__      =self.pad(lowercase_)
																		# Normalize

																		if self.size_divisibility > 0:
																									raise NotImplementedError()
																		# pad
																		a__      =torch.true_divide(lowercase_       ,  lowercase_)
																		if single_image:
																									return images[0], sizes[0], scales_yx[0]
																		else:
																									return images, sizes, scales_yx







def   _lowercase(					__a				:						str    ,     __a				:						Tuple     ):
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def   _lowercase(					__a				:						Dict    ,     __a				:						Tuple[int, int]     ):
							assert torch.isfinite(__a     ).all(), "Box tensor contains infinite or NaN!"
							a__    ,     a__      =box_size
							tensor[:, 0].clamp_(min=0    ,     max=__a     )
							tensor[:, 1].clamp_(min=0    ,     max=__a     )
							tensor[:, 2].clamp_(min=0    ,     max=__a     )
							tensor[:, 3].clamp_(min=0    ,     max=__a     )



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)

lowerCamelCase_    :			Dict    =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			"""dpr"""

			def __init__(							self			:							str    ,						snake_case_			:							Any=3_0522    ,						snake_case_			:							List[str]=768    ,						snake_case_			:							List[str]=12    ,						snake_case_			:							Union[str, Any]=12    ,						snake_case_			:							Tuple=3072    ,						snake_case_			:							Any="gelu"    ,						snake_case_			:							Optional[Any]=0.1    ,						snake_case_			:							str=0.1    ,						snake_case_			:							Dict=512    ,						snake_case_			:							int=2    ,						snake_case_			:							Union[str, Any]=0.02    ,						snake_case_			:							str=1e-12    ,						snake_case_			:							List[str]=0    ,						snake_case_			:							Any="absolute"    ,						snake_case_			:							int = 0    ,						**snake_case_			:							int    ,						):
							super().__init__(pad_token_id=snake_case_    ,						**snake_case_   )

							UpperCamelCase_:				str						=    vocab_size
							UpperCamelCase_:				Optional[int]						=    hidden_size
							UpperCamelCase_:				int						=    num_hidden_layers
							UpperCamelCase_:				Any						=    num_attention_heads
							UpperCamelCase_:				int						=    hidden_act
							UpperCamelCase_:				Optional[Any]						=    intermediate_size
							UpperCamelCase_:				Tuple						=    hidden_dropout_prob
							UpperCamelCase_:				Tuple						=    attention_probs_dropout_prob
							UpperCamelCase_:				str						=    max_position_embeddings
							UpperCamelCase_:				Any						=    type_vocab_size
							UpperCamelCase_:				Tuple						=    initializer_range
							UpperCamelCase_:				List[Any]						=    layer_norm_eps
							UpperCamelCase_:				Tuple						=    projection_dim
							UpperCamelCase_:				Dict						=    position_embedding_type






